Article2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)2023
Anomalous Behavior Detection in Trajectory Data of Older Drivers.
Article in 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Enhancing financial stability in healthcare through data-driven risk assessments with machine learning.Scientific reports · 2026Article
- Abnormal Driving Pattern Detection from GPS Trajectories Using Vision Transformer.Research square · 2026Article
- Pattern Mining of Older Drivers' Driving Behavior Through Telematics-data-driven Unsupervised Learning.IEEE sensors journal · 2025Article
- Endothelial c-Myc and Doxorubicin-Induced Metabolic Alterations: A Multi-Organ Optical Imaging Study.Journal of biophotonics · 2025Article
- Enhancing road safety: In-vehicle sensor analysis of cognitive impairment in older drivers.Multimedia tools and applications · 2025Article
- Quad-tree Based Driver Classification using Deep Learning for Mild Cognitive Impairment Detection.IEEE access : practical innovations, open solutions · 2025Article
- Spatial Deep Learning Approach to Older Driver Classification.IEEE access : practical innovations, open solutions · 2024Article
- Detection of mild cognitive impairment using various types of gait tests and machine learning.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hard-brakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.